Lesson 8 - REDD Case Study

This lesson shows us how to project deforestation rates based on socioeconomic variables. An econometric model specifies the statistical relationship between economic variables pertaining to a particular economic phenomena, in this case deforestation. It is useful to use an econometric model to simulate deforestation because it takes into consideration the many complex dynamic phenomena that produce deforestation as a result. These types of simulation models are important for evaluation of the potential for future deforestation under a business-as-usual scenario. In the case of this example, this is important in order for countries to receive incentives from reducing their carbon emissions from deforestation. REDD stands for “reducing emissions from deforestation and forest degradation” and is an idea that developing countries who do this over time, can receive financial compensation for doing so. This would require a country’s emission levels to be lower than historical levels and would require them to know historical emission levels and whether they were high or not. A model like this is important to predict future deforestation rates based on multiple economic variables to see if receiving compensation for REDD is a possibility for certain countries.

                The model used for this lesson contains a spatial lag regression that is applied to compute the influence of five variables on deforestation: Percentage of crop areas, cattle herd density, percent of protected areas, proximity to paved roads, and migration rates. The five variables listed are the five static variables that are used to determine the probability of deforestation. The spatial lag regression makes it so that the deforestation does not happen right off in respect to the spatial variables and instead occurs over time and spreads over time. This models “spatial neighborhood matrix” allows neighboring cells, in this case municipalities, to have an influence on the cell in question. This means that each cell is not independent of the surrounding cells and the variables in the surrounding cells will effect it. In terms of deforestation, this means that if one cell becomes deforested, then it is likely that neighboring cells will become deforested as well over time. Because of this, we would expect to see deforestation occur in expanding clumps instead of in random patches.

                After running the model with various cattle herd and crop rates, it was determined that the smaller the rates the better because this will produce a lower, steady rate of deforestation and CO2 emissions. A high rate of cattle herd and crops means a rapid increase of land use and therefore a rapid increase in deforestation and CO2 emissions and an overall unstable state.

                The model is simulated for 20 years and a map output is produced for each year. Below are three output maps from the model starting with the first year, the 10^th, and the last year:

                                                                                               

At first, it looks like these images are all the same and there is no difference. But looking at the amount of yellow in the first image compared to the last one, you can see that the yellow areas are more filled in and have less green areas between them. This shows that the areas surrounded by deforestation (yellow) eventually become deforested as well and that deforestation is similar to the spreading of a disease. The most worrying part about this is that in the first year, there were very small areas of deforestation in the middle of green (forested) patches. Those were very small and may not have caused many problems in the first year, but because of the spreading of deforestation, these areas have become much larger by the last year. At this point they will cause problems such as habitat fragmentation for wildlife species. It also seems that the deforestation has spread faster between 2010 and 2020 as opposed to 2001 and 2010. This is worrisome because it means that the rate of deforestation is increasing as time goes on. I could not get any data to show up for cattle herds, but below is a graph for annual deforestation and a graph for crops for years 2001, 2010, and 2020:

As you can see, the crop rates are similar every year, but steadily increase every year. The seasonal changes are always the same, but the overall annual rate for crops increases. For deforestation, however, the seasonal changes are not always the same. 2001 and 2010 are similar in that their peak deforestation rates were at the same time, but in the year 2020, the peaks were at the times where deforestation was lowest in the past. Overall, deforestation rates for 2020 do not seem to be higher than they were in previous years. In 2001 and 2010, deforestation rates seem to be the opposite of crop rates but in 2020, deforestation rates become a bit more similar to the seasonal changes of the crop rate.

In the scenario parameters group, I changed the cattle herd expansion and crop expansion values from 0.05 to 0.5. This simulates a rapid increase in land use. Below are the output maps for years 2001, 2010, and 2020 again:

  

As you can see in these output maps, the spread of deforestation occurs at a much higher rate and by 2020, a larger amount of forested areas have become deforested because of the demand for crops and cattle herds. It is impossible to see these images as something that is not a problem. The amount of forested land left after just 20 years is alarming. Below are the graphs for the annual crops and deforestation rates:

As you can see, for the crop rates, they started out low in 2001, increased a large amount by 2010, and then dropped down to 0 by 2020. With deforestation, the highest rates were in 2001 and they continually dropped in 2010 and even more in 2020. Maybe the rates for both the crops and deforestation decrease by 2020 because there is drastically less land to use for crops and deforestation because most of it has already been converted.

 

Next is estimating the carbon losses from the two scenarios presented above. This will be done with the carbon bookkeeping model which is a very common approach to estimating carbon emissions. This model calculates annual carbon emissions by determining annual deforestation (from our previous model) and overlaying these deforested areas on a map of forest carbon biomass. This model assumes that carbon content is 50% of wood biomass and 85% of carbon stored in trees is released into the atmosphere during deforestation. This model was run for both scenario 1 and scenario 2. The graphs for the annual carbon emissions in tons for each scenario, CO2 emissions, and the graphs for annual deforestation in hectares for each scenario are below:

As you can see, these graphs all look the same. With the small (0.05) cattle herd and crop rates, the annual rates of deforestation and carbon emissions were fairly steady without much fluctuation. With the rates much higher (0.5), the rates of deforestation and carbon emissions instantly were higher and fluctuated much more. Overall, the second scenario emitted much more CO2 into the atmosphere and much more deforestation took place. Therefore, it is best if the cattle herd and crop rates remained relatively low so the CO2 emissions and deforestation rates would remain low and steady.

White-tailed Deer Population Simulation Project

I would like to create a hypothetical model showing the spread of the deer population in Vermont if hunting was prohibited. The starting point would be a map of Vermont showing the current areas populated with deer and then I would have to do some sort of calculation using the current effect of hunting on the deer population and then a calculation with zero effect from hunting (because hypothetically there is no hunting). The output would then be a map of the population in however many years with the continued amount of hunting we have today and another map of the population in that many years without any hunting. 

After some difficult research on finding an input map for the current deer population in Vermont, I have run across a picture of a map of the U.S. with the white-tailed deer densities on the QDMA website. So I am hopefully in the process of getting my input map for my model. I then turned my research on similar publications done in Dinamica to figure out what my model should consist of. I looked at the publication called “Spatially explicit agent based model of rabbit population” which is a similar study on the spread of rabbit populations. This publication was a little confusing, but I was able to determine that my model also should be a spatially explicit (i.e. mimics environmental phenomena across space and time) agent based model (i.e. attempts to reproduce individual process of movement, behavior, birth, growth, and death according to a set of information). The authors took into account many attributes and parameters about the rabbits such as their : vision, energy spent to survive, maximum absorption of energy, energy spent to move, average lifespan, initial amount of calories, initial population, and maximum birth rate. When it comes to the landscape, they had it represented as a cell grid in which each cell contains a certain amount of resource. The attributes associated with the cells are the maximum capacity of resources (which are represented as the initial values on the landscape map) and the recovery rate of the landscape.

In the case of my project, I think I need to take similar if not the same attributes and parameters into consideration when trying to determine what a white-tailed deer population would look like without any hunting pressure. I also think that I would need to have some information on the suitability of the habitat in Vermont for deer in order to figure out how the population would spread. In the case of white-tailed deer, the resources needed to survive would be soft mast for foraging and wintering areas (which I found a map of in VT on the VT fish and wildlife website).  I think if those wintering areas have already been established then I can count those areas as suitable deer habitat for my model.

It is clear that much more research needs to be done on this project and it is much more complicated than I was thinking it would be. There is a lot of data that needs to go into this model in order to create a simulation of a spreading population and many parameters I hadn’t even thought of.

Spring Break in Texas

My spring break was spent in South Texas for a class called “Texas Wildlife Spring Break” (tough class, right?). This trip was 10 days long and we traveled from Corpus Christi and down around the border to Del Rio, and then back across to Corpus Christi. Being in Texas observing the environment and the wildlife, it was actually hard not to think about ecology. Most of what I thought about was how different the environment in Texas was than the environment of Vermont.  All of Texas was much drier and it seemed to get drier the more South we went. It was strange how Corpus Christi had palm trees and greener vegetation and as we traveled further, the vegetation started to get replaced with cacti and sparser. I thought it was interesting that Corpus Christi likely has a higher albedo than the Rio Grande Valley because there is greener vegetation in Corpus Christi and the Rio Grande Valley is part of the Chihuahuan desert which is mostly soil. The soil on the land surface is lighter than the green of the vegetation and therefore has a lower albedo.  I also thought about the differences in the soil in Texas versus the soil we see around here in Vermont.  In Texas, the soil seemed very sandy and rocky. I wonder if they were a different soil order than what we have here in Vermont…maybe aridisol? Perhaps the most interesting thing for me to see was that the difference in environment had such an effect on the plants and animals found in Texas. The plants and animals present in all of Texas are generally more tolerant to dry climates than the plants and animals found in Vermont.  It was also interesting to see that some animals were seen in Corpus Christi that were not seen in the Rio Grande Valley and vice versa. I’m so used to going birding in Vermont and seeing the birds in the vegetated forests and grasslands so it was weird walking around a desert where there was very little vegetation and still seeing birds and animals surviving there.

Exam Questions

1)      List and describe all six possible soil horizons.

Answer:

-The O layer is made up of undecomposed and decomposed organic material and humus.

-The A layer contains a high proportion of organic matter and is a leached mineral horizon.

-The E layer is lightly colored and is the zone of maximum leaching

-The B layer is the maximum zone of accumulation of weathering products like clay, silicate, and carbonates

-The C layer is relatively unaltered and is made of unconsolidated parent material

-The R layer is considered the hard bedrock layer

2)      Briefly explain the differences between C3, C4, and CAM plants and give some examples of each.

Answer:

-C3 plants have 2 molecules of three-carbon acid as the initial products of Carbon fixation. Most land plants are C3 plants. Examples: wheat, cotton, most trees

-C4 plants have a 2 stage strategy that keeps CO2 high and O2 low in chloroplast in order to maintain a vigorous Calvin cycle. This strategy uses malic acid and has a stage in the mesophyll cell and a stage in a bundle sheath cell. These plants can handle hot, dry conditions. Examples: Sugar cane, corn, crabgrass

-CAM plants are similar to C4 plants but their 2 stages are done at different times of the day. At night, the stomata open and malic acid is created and stored. During the day, the stomata are closed and the malic acid is used in the Calvin cycle. Examples: cacti, orchids, pineapple

3)      List the 4 dominating ecological processes in the dynamics of a system and determine which parts of the following situation fall under which ecological process:

A deer hunting plan has been put into place to control the density of the deer population in the state. The deer population eventually drops to an alarmingly low level. A change is made to the deer hunting plan to limit harvesting of the deer. The deer population begins to rise again.

Answer: The 4 processes are rapid growth, conservation, release, and reorganization. The deer hunting plan being put into place is the “rapid growth” stage. The population dropping to a low level is the “conservation” stage. The change made to the hunting plan is the “release” stage. The population growing in response is the “reorganization” stage.

Envisioning Environment at UVM - Undergraduate Level

The report I reviewed is “Envisioning Environment” at UVM. The purpose of this report is to develop an inventory and make recommendations about environmental research, education, and outreach at UVM.  The task had a broad focused on “environment, sustainability, and health” or ESH. I learned that the five major recommendations are to develop an ESH institute, create an associate provost ESH position, coordinate ESH graduate and undergraduate programs, expand graduate support, and finally to create an “environmental commons” or a physical and web-based place for ESH activity.

For the purposes of this blog, I focused my reading on the undergraduate education. I agree with the strengths related to ESH when it comes to undergraduate education. Being a part of the Rubenstein School, it is clear to me that we have many majors, minors, and classes related to various subjects relating to environmental health and its importance. There are many opportunities presented to students to become involved in the subject. I believe that we have a head start in that sense. I think it would also make sense for UVM to focus some time on marketing for our environmental education that we already have and try to spark some interest in the incoming students. I for one am very glad that I was drawn to the Rubenstein School and I think it is an area that will continue to expand and grow in the future. Something I’m not sure that I agree with, however, is that it is confusing to explain the range of options to incoming students. Sure, it can be confusing trying to decide on a major when you are coming into college, but I think all the information you need about each major is online. I think a “comprehensive advising map” would just be a waste of the school’s time and money when it comes to ESH.

It was helpful to see the view of UVM that this work group of people has and where we are in terms of environmental health. I think being in the Rubenstein School makes it easy for me to sort of assume that the whole school is constantly thinking about the environment and sustainability, but I have been reminded that isn’t the case and it is important that the awareness is spread.

Vegetation and Project Thinking

In my remote sensing class last semester, we did a lot of lessons using imagery from satellites to look at vegetation. A few of the corrections we focused on had to do with vegetation health. This reminds me of the MODIS images we looked at today in lecture showing the LAI values of different areas in South America. Until today I haven’t really been thinking about what I want to do for my project for this class, but I knew after taking remote sensing, the most interesting parts were always about vegetation.  Vegetation is always such an important topic because the basis of the carbon cycle, our source of oxygen and clean air, and even an indicator of soil, water, and air quality in an area. If there is a change at all in vegetation, it is something worth looking into.

I think it would be interesting to create a model to see how vegetation in a certain area changes over time. Whether vegetation levels decrease or increase is important, but it would also be interesting to see if the composition changes at all. Maybe natural species are slowly moving out while new species are slowly moving in or maybe the composition is staying the same. I’m still unaware of all the things Dinamica can do and all the possibilities the program has, but if there is a way to see how the species composition changes in an area over time, I think that would be an interesting project topic.

Talking about the movement of plant species also makes me wonder what exactly makes a plant an “invasive species”. The definition I know of invasive species is a species that is non-native to the ecosystem under consideration. We’ve heard over and over that ranges of plants and animals are changing with respect to climate change. If a plant that is not currently in Vermont suddenly slips over the border of a neighboring state into Vermont, is it an invasive species? Must it naturally occur in Vermont, or could it just be part of the natural movement patterns of the population? There was a time when no sugar maples or red oaks or any other plant for that matter existed in Vermont, so they all had to come in at some point…but that doesn’t make any of them invasive species?

Soils

The topic of soils was never all that interesting to me, to be honest. I have not taken a soils class in college, my only experience with learning about soils comes from a brief overview in biology and a high school earth science class. The only things I remember learning from these classes about soil is the different soil horizons and the differences between those horizons. In class on Tuesday was the first time I have ever heard about soil orders and those made the topic of soil a little less boring. It was interesting to see the effects that climate, weather and placement has on the soil and that those three things are essentially the deciding factors for which order of soil you will find in an area. It would be interesting to see the effects of climate change over the years on the soil types. Would they change at all and is it possible for the order of the soil to change?

                Another aspect of the soils that I found fascinating was the serious of slides showing the “black soils” of the south. At one point in the Cretaceous period, that area of Georgia and Alabama was the shore which caused the soils to be different than the surrounding soils. Even after the shoreline moved outward, the soils remained the same and the graph showed that this was the area with the best cotton crop and with a higher African American population. Eventually, the people living in this “black soils” ring became more democratic and voted for Obama while surrounding areas did not. It was very interesting to see the correlation between all these things and soils because it is something I never would have thought would be related. It just shows that where you are raised and the soils you live on have an impact on who you are. I have learned that soils are much more important in countless aspects than I ever thought.